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As the 7th Earth Explorer Mission within the ESA Earth Observation Program, BIOMASS will be the first spaceborne P-band polarimetric interferometric SAR (PolInSAR) mission, dedicated to global forest remote sensing. Compared with airborne PolInSAR, BIOMASS PolInSAR faces several intrinsic limitations, including the reduced number of baselines, low bandwidth of 6 MHz, and the 3-day repeat-pass acquisition mode. In this study, the capabilities of three-baseline PolInSAR in BIOMASS configuration and the spaceborne system-induced impacts are systematically evaluated. First, the BIOMASS data were simulated using the airborne P-band SAR acquisitions collected over two different tropical forests in Paracou, French Guiana, and Mondah, Gabon. Second, the forest height was retrieved using a three-baseline algorithm based on polarimetric coherence optimization with multi-baseline joint diagonalization. Finally, the impacts of spaceborne PolInSAR were quantified and analyzed. The results show that the performance of PolInSAR inversion in the spaceborne configuration decreases compared to that of the original airborne data. However, the three-baseline method still yields acceptable results, with a root-mean-square error ranging from 4.92 to 6.07 m and a correlation coefficient (R2) from 0.32 to 0.85, within hectare-scale forest height statistics. This study demonstrates that the limited bandwidth of BIOMASS has a certain impact on refined forest structure parameter retrieval, while the three-baseline PolInSAR method remains effective for large-scale forest mapping, with accuracy meeting the design requirements of the BIOMASS mission.

Remote sensing satellites allow large-scale and fast detections of forest loss. Operational forest loss detection systems have been mainly developed over tropical forests; however, it is increasingly important to have access to accurate and up-to-date information on temperate forests. In this article, we adapted a Sentinel-1-based near real-time tropical forest loss detection method, based on the radar change ratio, to detect French temperate forests clear-cuts. Using ancillary data, annual and submonthly clear-cuts were assessed for broadleaf and conifer forests, for various tree species, over public and private forests. Using 967 validation plots, the maps exhibited recall and precision of 80.9% and 99.4%, respectively. The clear-cuts area shows remarkable stability over time from 2020. We found seven times more clear-cuts in private forests than in public forests, although the surface area of private forests is only three times that of public forests. It was also demonstrated that only 1.6% out of 4 530 dieback reference plots, and 6.2% of maps of forest bark beetle attacks, were confused with clear-cuts before clear-cuts actually occurred, which makes our maps complementary with forest dieback maps. Collectively, the findings of this study could have significant implications for the implementation of a radar-satellite-based system designed for the real-time detection of large-scale clear-cuts in European temperate forests.

There is growing interest in using passive microwave observations and vegetation optical depth (VOD) to study the above-ground biomass (AGB) and carbon stocks evolution. L-band observations, in particular, have been shown to be very sensitive to AGB. Here, thanks to the multiangle capabilities of the soil moisture and ocean salinity mission, a new approach to estimate AGB directly from multiangular L-band brightness temperatures (TBs) is proposed, thus surpassing the use of intermediate variables such as VOD. The European Space Agency (ESA) Climate Change Initiative (CCI) Biomass maps for the years 2010, 2017, and 2018 are used as the AGB reference. AGB estimates from artificial neural networks (ANN) using a purely data-driven approach explained up to 88% of AGB variability globally; even so, a decrease in retrieval performance was observed when models are applied to data from years different than the year used for their training. A new training methodology based on multiyear training sets is presented, leading to results showing more stability for temporal analyses. The best set of predictors and an optimal learning dataset configuration are proposed based on an assessment of the accuracy of the estimates. The ANN methodology using TBs is a promising alternative with respect to the common method of using a parametric function to estimate AGB from VOD. ANNs AGB estimates showed a higher correlation with CCI AGB maps ($R$2 $\sim$0.87 instead of $\sim$0.84) and presented a stronger agreement with their spatial structure and less differences in residual maps.

The aim of this study was to develop a robust methodology for evaluating the spatiotemporal dynamics of the inundation status in tropical wetlands with the currently available Global Navigation Satellite System-Reflectometry (GNSS-R) data by proposing a new quality control technique called the “precision index”. The methodology was applied over the Mekong Delta, one of the most important rice-production systems comprising aquaculture areas and natural wetlands (e.g., mangrove forests, peatlands). Cyclone Global Navigation Satellite System (CyGNSS) constellation data (August 2018–December 2021) were used to evaluate the spatiotemporal dynamics of the reflectivity Γ over the delta. First, the reflectivity Γ, shape and size of each specular footprint and the precision index were calibrated at each specular point and reprojected to a 0.0045° resolution (approximately equivalent to 500 m) grid at a daily temporal resolution (Lv. 2 product); then, the results were obtained considering bias-causing factors (e.g., the velocity/effective scattering area/incidence angle). The Lv. 2 product was temporally integrated every 15 days with a Kalman smoother (+/− 14 days temporal localization with Gaussian kernel: 1σ = 5 days). By applying the smoother, the regional-annual dynamics over the delta could be clearly visualized. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 Phased-Array type L-band Synthetic Aperture Radar-2 quadruple polarimetric scatter signals were compared and found to be nonlinearly correlated due to the influence of the incidence angle and the effective scattering area.

The penetration capability of P-band radar waves through dense vegetation, along with the ability of tomography to separate the contributions of different layers in a vertical reflectivity profile, make P-band radar tomography a promising tool for digital terrain modeling in forested areas, specifically in dense tropical forests under which terrain topography remains poorly known. This paper aims to assess the overall quality of a digital terrain model (DTM) produced using tomographic processing of airborne P-band SAR imagery acquired during the TropiSAR campaign in French Guiana. Many quality descriptors are used to evaluate the quality of this DTM. Position and slope accuracies are computed based on a lidar DTM considered as the reference, and the impact of several parameters on these accuracies is studied, namely, slope, slope orientation, off-nadir angle and local incidence angle. The realism of the landforms is also studied according to geomorphological criteria. The results of this multicriteria accuracy assessment show the high potential of P-band SAR tomography in depicting the topography under forests, despite the intrinsic limitations related to the slant range geometry: the absolute elevation error is around 2 m; the slope is overestimated with an error of about 16°, mainly due to a processing artifact for which easy and direct solutions exist. Indeed, this error is equal to about 3° in flat artifact-free areas. These errors vary depending on the acquisition parameters and the local topography. The shapes are globally well preserved. These results are also discussed in the frame of the upcoming BIOMASS mission developed by the European Space Agency (ESA) and expected to be launched in 2024.

Forests are one of the key elements in ecological transition policies in Europe. Sustainable forest management is needed in order to optimise wood harvesting, while preserving carbon storage, biodiversity and other ecological functions. Forest managers and public bodies need improved and cost-effective forest monitoring tools. Research studies have been carried out to assess the use of optical and radar images for producing forest height or biomass maps. The main limitations are the quantity, quality and representativeness of the reference data for model training. The Global Ecosystem Dynamics Investigation (GEDI) mission (full waveform LiDAR on board the International Space Station) has provided an unprecedented number of forest canopy height samples from 2019. These samples could be used to improve reference datasets. This paper aims to present and validate a method for estimating forest dominant height from open access optical and radar satellite images (Sentinel-1, Sentinel-2 and ALOS-2 PALSAR-2), and then to assess the use of GEDI samples to replace field height measurements in model calibration. Our approach combines satellite image features and dominant height measurements, or GEDI metrics, in a Support Vector Machine regression algorithm, with a feature selection process. The method is tested on mixed uneven-aged broadleaved and coniferous forests in France. Using dominant height measurements for model training, the cross-validation shows 7.3 to 11.6% relative Root Mean Square Error (RMSE) depending on the forest class. When using GEDI height metrics instead of field measurements for model training, errors increase to 12.8–16.7% relative RMSE. This level of error remains satisfactory; the use of GEDI could allow the production of dominant height maps on large areas with better sample representativeness. Future work will focus on confirming these results on new study sites, improving the filtering and processing of GEDI data, and producing height maps at regional or national scale. The resulting maps will help forest managers and public bodies to optimise forest resource inventories, as well as allow scientists to integrate these cartographic data into climate models.

Lakes play a key role in the global water cycle, providing essential water resources and ecosystem services for humans and wildlife. Quantifying long-term changes in lake volume at a global scale is therefore important to the sustainability of humanity and natural ecosystems. Yet, such an estimate is still unavailable because, unlike lake area, lake volume is three-dimensional, challenging to be estimated consistently across space and time. Here, taking advantage of recent advances in remote sensing technology, especially NASA’s ICESat-2 satellite laser altimeter launched in 2018, we generated monthly volume series from 2003 to 2020 for 9065 lakes worldwide with an area ≥ 10 km2. We found that the total volume of the 9065 lakes increased by 597 km3 (90% confidence interval 239–2618 km3). Validation against in situ measurements showed a correlation coefficient of 0.98, an RMSE (i.e., root mean square error) of 0.57 km3 and a normalized RMSE of 2.6%. In addition, 6753 (74.5%) of the lakes showed an increasing trend in lake volume and were spatially clustered into nine hot spots, most of which are located in sparsely populated high latitudes and the Tibetan Plateau; 2323 (25.5%) of the lakes showed a decreasing trend in lake volume and were clustered into six hot spots—most located in the world’s arid/semi-arid regions where lakes are scarce, but population density is high. Our results uncovered, from a three-dimensional volumetric perspective, spatially uneven lake changes that aggravate the conflict between human demands and lake resources. The situation is likely to intensify given projected higher temperatures in glacier-covered regions and drier climates in arid/semi-arid areas. The 15 hot spots could serve as a blueprint for prioritizing future lake research and conservation efforts.

In this study, we demonstrate the ability of a new operational system to detect forest loss at a large scale accurately and in a timely manner. We produced forest loss maps every week over Vietnam, Cambodia, and Laos (>750,000 km2 in total) using Sentinel-1 data. To do so, we used the forest loss detection method based on shadow detection. The main advantage of this method is the ability to avoid false alarms, which is relevant in Southeast Asia where the areas of forest disturbance may be very small and scattered and detection is used for alert purposes. The estimated user accuracy of the forest loss map was 0.95 for forest disturbances and 0.99 for intact forest, and the estimated producer’s accuracy was 0.90 for forest disturbances and 0.99 for intact forest, with a minimum mapping unit of 0.1 ha. This represents an important step forward compared to the values achieved by previous studies. We also found that approximately half of forest disturbances in Cambodia from 2018 to 2020 occurred in protected areas, which emphasizes the lack of efficiency in the protection and conservation of natural resources in protected areas. On an annual basis, the forest loss areas detected using our method are found to be similar to the estimations from Global Forest Watch. These results highlight the fact that this method provides not only quick alerts but also reliable detections that can be used to calculate weekly, monthly, or annual forest loss statistics at a national scale.

Rice is the primary staple food of more than half of the world’s population and plays an especially important role in global economy, food security, water use, and climate change. The usefulness of Synthetic Aperture Radars (SAR) for rice mapping and monitoring has been demonstrated locally in many studies, in particular in the last five years with the availability of an unprecedented amount of free Sentinel-1 data within the Copernicus program. However, although earlier studies from the 1990s have laid the foundations of the physical understanding of the SAR response of rice fields, the more recent studies tend to overlook this aspect and to favor instead approaches driven by supervised learning which provide accurate results locally but cannot necessarily be extended to wide areas. The objective of this study is to analyze in detail the backscatter temporal variation of rice fields, using Sentinel-1 from 2015 to 2020 and in-situ data for the 5 rice seasons over 2 years 2017–2018, in order to derive robust SAR-based indicators useful for rice monitoring applications, which are essential for planning, monitoring and food security applications. The test region is the An Giang province, in the Mekong River Delta, Vietnam, one of the world’s major rice regions which presents a diversity in rice cultivation practices, in cropping density, and in crop calendar. The SAR data have been analyzed as a function of rice parameters, and the temporal and polarization behaviors of the radar backscatter of different rice varieties have been interpreted physically. New backscatter indicators for the detection of rice paddy area, the estimation of the sowing date, phenological stage and the mapping of the short cycle and long cycle rice varieties have been developed and discussed regarding the generality of the methods with respect to the rice cultural practices and the SAR data characteristics.

BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements.

The rapidly developing urbanization since the last decade of the 20th century has led to extensive groundwater extraction, resulting in subsidence in Ho Chi Minh City, Vietnam. Recent advances in multi-temporal spaceborne SAR interferometry, especially with a persistent scatters interferometry (PSI) approach, has made this a robust remote sensing technique for measuring large-scale ground subsidence with millimetric accuracy. This work has presented an advanced PSI analysis, to provide an unprecedented spatial extent and continuous temporal coverage of the subsidence in Ho Chi Minh City from 2006 to 2010. The study shows that subsidence is most severe in the Holocene silt loam areas along the Sai Gon River and in the southwest of the city. The groundwater extraction resulting from urbanization and urban growth is mainly responsible for the subsidence. Subsidence in turn leads to more flooding and water nuisance. The correlation between the reference leveling velocity and the estimated PSI result is R2 = 0.88, and the root mean square error is 4.3 (mm/year), confirming their good agreement. From 2006 to 2010, the estimation of the average subsidence rate is -8.0 mm/year, with the maximum value up to -70 mm/year. After four years, in regions along Sai Gon River and in the southwest of the city, the land has sunk up to -12 cm. If not addressed, subsidence leads to the increase of inundation, both in frequency and spatial extent. Finally, regarding climate change, the effects of subsidence should be considered as appreciably greater than those resulting from rising sea level. It is essential to consider these two factors, because the city is inhabited by more than 7.5 million people, where subsidence directly impacts urban structures and infrastructure.

Temperate forests are under climatic and economic pressures. Public bodies, NGOs and the wood industry are looking for accurate, current and affordable data driven solutions to intensify wood production while maintaining or improving long term sustainability of the production, biodiversity, and carbon sequestration. Free tools and open access data have already been exploited to produce accurate quantitative forest parameters maps suitable for policy and operational purposes. These efforts have relied on different data sources, tools, and methods that are tailored for specific forest types and climatic conditions. We hypothesized we could build on these efforts in order to produce a generic method suitable to perform as well or better in a larger range of settings. In this study we focus on building a generic approach to create forest parameters maps and confirm its performance on a test site: a maritime pine (Pinus pinaster) forest located in south west of France. We investigated and assessed options related with the integration of multiple data sources (SAR L- and C-band, optical indexes and spatial texture indexes from Sentinel-1, Sentinel-2 and ALOS-PALSAR-2), feature extraction, feature selection and machine learning techniques. On our test case, we found that the combination of multiple open access data sources has synergistic benefits on the forest parameters estimates. The sensibility analysis shows that all the data participate to the improvements, that reach up to 13.7% when compared to single source estimates. Accuracy of the estimates is as follows: aboveground biomass (AGB) 28% relative RMSE, basal area (BA) 27%, diameter at breast height (DBH) 20%, age 17%, tree density 24%, and height 13%. Forward feature selection and SVR provided the best estimates. Future work will focus on validating this generic approach in different settings. It may prove beneficial to package the method, the tools, and the integration of open access data in order to make spatially accurate and regularly updated forest structure parameters maps effortlessly available to national bodies and forest organizations.

In this study, the potential of Sentinel-1 data to seasonally monitor temperate forests was investigated by analyzing radar signatures observed from plots in the Fontainebleau Forest of the Ile de France region, France, for the period extending from March 2015 to January 2016. Radar backscattering coefficients, σ0 and the amplitude of temporal interferometric coherence profiles in relation to environmental variables are shown, such as in situ precipitation and air temperature. The high temporal frequency of Sentinel-1 acquisitions (i.e., twelve days, or six, if both Sentinel-1A and B are combined over Europe) and the dual polarization configuration (VV and VH over most land surfaces) made a significant contribution. In particular, the radar backscattering coefficient ratio of VV to VH polarization, σ V V 0 / σ V H 0 , showed a well-pronounced seasonality that was correlated with vegetation phenology, as confirmed in comparison to NDVI profiles derived from Landsat-8 (r = 0.77) over stands of deciduous trees. These results illustrate the high potential of Sentinel-1 data for monitoring vegetation, and as these data are not sensitive to the atmosphere, the phenology could be estimated with more accuracy than optical data. These observations will be quantitatively analyzed with the use of electromagnetic models in the near future.

To detect deforestation using Earth Observation (EO) data, widely used methods are based on the detection of temporal changes in the EO measurements within the deforested patches. In this paper, we introduce a new indicator of deforestation obtained from synthetic aperture radar (SAR) images, which relies on a geometric artifact that appears when deforestation happens, in the form of a shadow at the border of the deforested patch. The conditions for the appearance of these shadows are analyzed, as well as the methods that can be employed to exploit them to detect deforestation. The approach involves two steps: (1) detection of new shadows; (2) reconstruction of the deforested patch around the shadows. The launch of Sentinel-1 in 2014 has opened up opportunities for a potential exploitation of this approach in large-scale applications. A deforestation detection method based on this approach was tested in a 600,000 ha site in Peru. A detection rate of more than 95% is obtained for samples larger than 0.4 ha, and the method was found to perform better than the optical-based UMD-GLAD Forest Alert dataset both in terms of spatial and temporal detection. Further work needed to exploit this approach at operational levels is discussed.

In this paper, we aim to assess the impacts of the forest definitions adopted by each African country involved in the global climate change programmes of the United Nations on national carbon emission estimations. To do so, we estimate the proportion of national carbon stocks and tree cover loss that are found in areas considered to be non-forest areas. These non-forest areas are defined with respect to a threshold on the percentage of tree cover adopted by each country. Using percent tree cover and aboveground biomass maps derived from remote sensing data, we quantitatively show that in many countries, a large proportion of carbon stocks are found in non-forest areas, where a large amount of tree cover loss can also occur. We further found that under the REDD+ framework (reduced deforestation, reduced degradation, enhancement and conservation of forest carbon stocks, sustainable management of forests), some partner countries have proposed activities related to only reducing deforestation, even when a large proportion of their carbon stocks are stored outside forests. This situation may represent a limitation of the efficiency of the REDD+ mechanism, and could be avoided if these countries choose a lower tree cover threshold for their definition of forests and/or if they are engaged in other activities.

Rice is a major staple food for nearly half of the world’s population and has a considerable contribution to the global agricultural economy. While spaceborne Synthetic Aperture Radar (SAR) data have proved to have great potential to provide rice cultivation area, few studies have been performed to provide practical information that meets the user requirements. In rice growing regions where the inter-field crop calendar is not uniform such as in the Mekong Delta in Vietnam, knowledge of the start of season on a field basis, along with the planted rice varieties, is very important for correct field management (timing of irrigation, fertilization, chemical treatment, harvest), and for market assessment of the rice production. The objective of this study is to develop methods using SAR data to retrieve in addition to the rice grown area, the sowing date, and the distinction between long and short cycle varieties. This study makes use of X-band SAR data from COSMO-SkyMed acquired from 19 August to 23 November 2013 covering the Chau Thanh and Thoai Son districts in An Giang province, Viet Nam, characterized by a complex cropping pattern. The SAR data have been analyzed as a function of rice parameters, and the temporal and polarization behaviors of the radar backscatter of different rice varieties have been interpreted physically. New backscatter indicators for the detection of rice paddy area, the estimation of the sowing date, and the mapping of the short cycle and long cycle rice varieties have been developed and assessed. Good accuracy has been found with 92% in rice grown area, 96% on rice long or short cycle, and a root mean square error of 4.3 days in sowing date. The results have been discussed regarding the generality of the methods with respect to the rice cultural practices and the SAR data characteristics.

L'ONERA, le laboratoire français en aérospatial, construit des systèmes radar aéroporté depuis plus de deux décennies pour des applications de défense mais aussi scientifiques. Au cours des six dernières années, l'instrument SETHI, embarqué à bord d'un avion Falcon 20, a été développé principalement pour des applications scientifiques. Dans cet article, la philosophie de son développement est mise en exergue et les instruments sont décrits en détails. L'instrument SETHI comprend trois systèmes radar en bandes P, L et X, une caméra dans le domaine visible, en visée oblique afin de suivre le champ de vue des radar, et une caméra hyperspectrale à visée au nadir. Cet article résume alors les dernières campagnes SETHI dans le cas de préparation de deux missions spatiales. Il s'agit tout d'abord de la mission de l'ESA, BIOMASS, comprenant un radar bande P pour la cartographie de la biomasse à grande échelle. La deuxième mission traite de surveillance maritime et comprend un radar en bande X. Pour chaque campagne, les principaux objectifs sont détaillés, les références aux articles correspondants sont fournies et les principaux résultats sont mis en avant. Finalement, l'article fournit un aperçu des prochains développements liés au système SETHI dans le cadre de la fusion de données optique et radar.

This paper aims to develop a new methodology for monitoring forest disturbances and regrowth using ALOS PALSAR data in tropical regions. In the study, forest disturbances and regrowth were assessed between 2007 and 2010 in Vietnam, Cambodia and Lao People’s Democratic Republic. The deforestation rate in Vietnam has been among the highest in the tropics in the last few decades, and those in Cambodia and Lao are increasing rapidly. L-band ALOS PALSAR mosaic data were used for the detection of forest disturbances and regrowth, because L-band SAR intensities are sensitive to forest aboveground biomass loss. The methodology used here combines SAR data processing, which is particularly suited for change detection, forest detection and forest disturbances and regrowth detection using expectation maximization, which is closely related to fuzzy logic. A reliable training and testing database has been derived using AVNIR-2 and Google Earth images for calibration and validation. Efforts were made to apply masking areas that are likely to show different SAR backscatter temporal behaviors from the forests considered in the study, including mangroves, inundated forests, post-flooding or irrigated croplands and water bodies, as well as sloping areas and urban areas. The resulting forest disturbances and regrowth map (25-m resolution) indicates disturbance rates of −1.07% in Vietnam, −1.22% in Cambodia and −0.94% in Lao between 2007 and 2010, with corresponding aboveground biomass losses of 60.7 Tg, 59.2 Tg and 83.8 Tg , respectively. It is expected that the method, relying on free of charge data (ALOS and ALOS2 mosaics), can be applied widely in the tropics.